An Untapped Opportunity
As we transition into the home stretch of 2015, it is clear that top line growth remains the No. 1 priority of senior executives at consumer products companies both large and small, and in every product category. And finding new sources of growth is not getting any easier for most established consumer products companies due to fundamental changes in the traditional levers of competitive advantage. Traditional sources of advantage in areas like economies of scale in manufacturing and distribution are becoming less important due to the growing ecosystem of available partners and the ability to leverage a direct to consumer business model to quickly gain scale. Think of how quickly companies like Dollar Shave Club and The Honest Co. have grown and it’s easy to realize that the barriers to entry are lower and the overall competitive intensity is significantly higher relative to what it was only five years ago.
One strategy that we see being adopted by leading consumer products companies is the utilization of a rich, comprehensive set of external data sources to drive improvements in sales, marketing and distribution activities at a hyper-local level (e.g. weather, sporting and community events, competitor activity, and consumer attitudes and behaviors). For example, a leading food and beverage company is able to identify, predict and act upon purchase triggers related to weather and local community event data (e.g., neighborhood 5k races or a local high school football game) at a zip+4 level by day and day part — for each of their target consumer segments. This enables them to execute precision marketing actions by sending the right mobile offer to the right individual consumer, at the right time and place. This has resulted in substantially improved marketing effectiveness driven by the company’s ability to model and act upon these hyper-local external drivers of purchase behavior.
The utility of this type of insight into the impact of hyper-local external factors applies well beyond marketing. For example, another consumer products company is leveraging a similar approach to better predict potential out-of-stock situations and improve DSD replenishment based on predicting the impact of local factors such as ambient temperature and humidity conditions coupled with the location of specific stores to model the variance of demand on ice cream products based on different temperature/humidity/day/day part combinations at a hyper-local store level.
The integration and analysis of external data sets such as hyper-local weather, event, competitor, and consumer data is not simple. Equally challenging is the generation of real business benefit by turning these insights into action — for as Thomas Edison said, “the value of an idea lies in the using of it.” However, by measuring, modeling and acting upon the impact of local external forces, consumer products companies can unlock precious sources of untapped incremental growth.
One strategy that we see being adopted by leading consumer products companies is the utilization of a rich, comprehensive set of external data sources to drive improvements in sales, marketing and distribution activities at a hyper-local level (e.g. weather, sporting and community events, competitor activity, and consumer attitudes and behaviors). For example, a leading food and beverage company is able to identify, predict and act upon purchase triggers related to weather and local community event data (e.g., neighborhood 5k races or a local high school football game) at a zip+4 level by day and day part — for each of their target consumer segments. This enables them to execute precision marketing actions by sending the right mobile offer to the right individual consumer, at the right time and place. This has resulted in substantially improved marketing effectiveness driven by the company’s ability to model and act upon these hyper-local external drivers of purchase behavior.
The utility of this type of insight into the impact of hyper-local external factors applies well beyond marketing. For example, another consumer products company is leveraging a similar approach to better predict potential out-of-stock situations and improve DSD replenishment based on predicting the impact of local factors such as ambient temperature and humidity conditions coupled with the location of specific stores to model the variance of demand on ice cream products based on different temperature/humidity/day/day part combinations at a hyper-local store level.
The integration and analysis of external data sets such as hyper-local weather, event, competitor, and consumer data is not simple. Equally challenging is the generation of real business benefit by turning these insights into action — for as Thomas Edison said, “the value of an idea lies in the using of it.” However, by measuring, modeling and acting upon the impact of local external forces, consumer products companies can unlock precious sources of untapped incremental growth.